Assumption-Based Leading Indicators in Healthcare Governance
This peer-reviewed publication presents a structured human factors and systems engineering approach for detecting organisational drift in complex healthcare environments before patient harm escalates
Using the Mid Staffordshire NHS failure as a systems case study, the research applies a STAMP-based hierarchical control model to identify:
- Invalid governance assumptions
- Feedback breakdowns across oversight levels
- Drift in safety constraint enforcement
- Cognitive bias within decision pathways
Why This Matters for Healthcare Leaders
Healthcare failures rarely occur suddenly They emerge gradually through relaxed safeguards, misaligned incentives, and inaccurate feedback loops
When performance signals are accepted without testing underlying assumptions, systemic drift becomes normalised
This research underpins Enginova Health Systems’ methodology for:
- Independent safety risk reviews
- Governance analysis
- Control structure evaluation
- Leading indicator development
- Regulatory audit preparation
Assumption-Based Framework
The framework applies a STAMP-based hierarchical control structure to identify where governance assumptions become invalid over time
It examines feedback loops and control actions to detect when organisational safeguards degrade or diverge from their intended design constraints
Case Study Insights
Analysis of the Mid Staffordshire NHS failure demonstrates how inaccurate feedback, over-reliance on limited performance indicators, and unchallenged assumptions contributed to progressive system drift across oversight levels
Practical Applications
The approach supports the development of assumption-based leading indicators that test whether operational conditions continue to match the original safety expectations embedded within governance structures
Benefits for Healthcare Leaders
By identifying when feedback signals are incomplete, selectively interpreted, or misaligned with system realities, leaders can intervene before safety constraints are weakened and adverse outcomes emerge
This publication explores how assumption-based leading indicators can be applied to monitor healthcare system drift toward failure
Published in Ergonomics & Human Factors 2023 (CIEHF Proceedings)